1 TADPOLE and BSWiMS

1.0.1 Loading the libraries

library("FRESA.CAD")
library(survival)
library(readxl)
library(igraph)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)

1.1 Loading BSWiMS Results


load("./TADPOLE_BSWIMS_Results.RData")

pander::pander(table(TADPOLE_Conv_TRAIN$status))
0 1
261 133 
pander::pander(table(TADPOLE_Conv_TEST$status))
0 1
112 58 
par(op)

1.1.1 Cross Validation BIC


cvBESSRaw <- randomCV(TADPOLECrossMRI,
                 Surv(TimeToEvent,status)~.,
                 fittingFunction= BESS,
                 trainSampleSets= cvBSWiMSRaw$trainSamplesSets,
 )

……….10 Tested: 552 Avg. Selected: 51 Min Tests: 1 Max Tests: 10 Mean Tests: 5.036232 . MAD: 1.173326 ……….20 Tested: 562 Avg. Selected: 51 Min Tests: 1 Max Tests: 17 Mean Tests: 9.893238 . MAD: 1.11624 ……….30 Tested: 564 Avg. Selected: 51 Min Tests: 3 Max Tests: 25 Mean Tests: 14.78723 . MAD: 1.117324 ……….40 Tested: 564 Avg. Selected: 51 Min Tests: 4 Max Tests: 32 Mean Tests: 19.71631 . MAD: 1.115381 ……….50 Tested: 564 Avg. Selected: 51 Min Tests: 6 Max Tests: 39 Mean Tests: 24.64539 . MAD: 1.115094


pander::pander(cbind(cvBESSRaw$featureFrequency[cvBESSRaw$featureFrequency>20]))
FAQ 50
RAVLT_immediate 48
ADAS13 45
RD_ST50TS 42
RD_ST34TA 41
RD_ST47TS 41
ABETA 38
RD_ST45TA 34
M_ST66SV 31
RD_ST31TA 31
RD_ST32TS 30
RD_ST58CV 30
RD_ST12SV 29
RD_ST129TS 27
RD_ST51SA 27
APOE4 26
M_ST62SA 26
PTAU 26
RD_ST25TS 26
RD_ST35TS 26
M_ST53SV 25
RD_ST44CV 25
RD_ST24SA 24
RD_ST60TS 22
RD_ST15TA 21 

prBin <- predictionStats_binary(cvBESSRaw$survMedianTrain[,c(2,3)],"TRAIN: MCI to  AD Conversion")


survmtest <- cvBESSRaw$survMedianTest
survmtest <- survmtest[complete.cases(survmtest),]

prBin <- predictionStats_binary(survmtest[,c(2,3)],"BESS:BIC: MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.832 0.798 0.867 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 150 106 256
Test - 41 267 308
Total 191 373 564 

par(op)
ho <- mean(survmtest$Outcome)
timeInterval <- mean(survmtest[survmtest$Outcome==0,"Times"])
pgzero <- ppoisGzero(survmtest$LinearPredictorsMedian,ho)
rsdata <- cbind(survmtest$Outcome,pgzero,survmtest$Times)
riskAnalysis <- RRPlot(rsdata,riskTimeInterval=timeInterval,title="BESS:BIC")

[1] 0.3386525 [1] 0.3386525 1.0000000 [1] 0.7771272 0.8672770 0.3942505 3.4912148 49.5056709 101.3358977 [7] 0.0000000 1.0000000 


pander::pander(riskAnalysis$c.index)

  • C Index: 0.818

  • Dxy: 0.636

  • S.D.: 0.0278

  • n: 564

  • missing: 0

  • uncensored: 191

  • Relevant Pairs: 142910

  • Concordant: 116890

  • Uncertain: 174472

  • cstatCI:

    mean.C Index median lower upper
    0.818 0.817 0.788 0.844 
pander::pander(riskAnalysis$ROCAnalysis$aucs)
est lower upper
0.831 0.796 0.866 
pander::pander(riskAnalysis$cenAUC)

0.853

pander::pander(riskAnalysis$ROCAnalysis$ClassMetrics)

  • accci:

    50% 2.5% 97.5%
    0.785 0.752 0.817

  • senci:

    50% 2.5% 97.5%
    0.634 0.561 0.7

  • speci:

    50% 2.5% 97.5%
    0.864 0.828 0.898

  • aucci:

    50% 2.5% 97.5%
    0.749 0.709 0.786

  • berci:

    50% 2.5% 97.5%
    0.251 0.214 0.291

  • preci:

    50% 2.5% 97.5%
    0.703 0.633 0.771

  • F1ci:

    50% 2.5% 97.5%
    0.667 0.607 0.719 
pander::pander(riskAnalysis$surdif)
Call: survival::Surv(eTime, eStatus) ~ class Chisq = 252.572826 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 334 49 133.7 53.6913 183.7155
class=1 58 21 20.3 0.0271 0.0306
class=2 172 121 37.0 190.6823 244.3976

1.1.2 Cross Validation EBIC


cvBESSERaw <- randomCV(TADPOLECrossMRI,
                 Surv(TimeToEvent,status)~.,
                 fittingFunction= BESS_EBIC,
                 trainSampleSets= cvBSWiMSRaw$trainSamplesSets,
 )

……….10 Tested: 552 Avg. Selected: 51 Min Tests: 1 Max Tests: 10 Mean Tests: 5.036232 . MAD: 1.173326 ……….20 Tested: 562 Avg. Selected: 51 Min Tests: 1 Max Tests: 17 Mean Tests: 9.893238 . MAD: 1.11624 ……….30 Tested: 564 Avg. Selected: 51 Min Tests: 3 Max Tests: 25 Mean Tests: 14.78723 . MAD: 1.117324 ……….40 Tested: 564 Avg. Selected: 51 Min Tests: 4 Max Tests: 32 Mean Tests: 19.71631 . MAD: 1.115381 ……….50 Tested: 564 Avg. Selected: 51 Min Tests: 6 Max Tests: 39 Mean Tests: 24.64539 . MAD: 1.115094

pander::pander(cbind(cvBESSERaw$featureFrequency[cvBESSERaw$featureFrequency>20]))
FAQ 50
RAVLT_immediate 48
ADAS13 45
RD_ST50TS 42
RD_ST34TA 41
RD_ST47TS 41
ABETA 38
RD_ST45TA 34
M_ST66SV 31
RD_ST31TA 31
RD_ST32TS 30
RD_ST58CV 30
RD_ST12SV 29
RD_ST129TS 27
RD_ST51SA 27
APOE4 26
M_ST62SA 26
PTAU 26
RD_ST25TS 26
RD_ST35TS 26
M_ST53SV 25
RD_ST44CV 25
RD_ST24SA 24
RD_ST60TS 22
RD_ST15TA 21 

prBin <- predictionStats_binary(cvBESSERaw$survMedianTrain[,c(2,3)],"TRAIN: MCI to  AD Conversion")


survmtest <- cvBESSERaw$survMedianTest
survmtest <- survmtest[complete.cases(survmtest),]

prBin <- predictionStats_binary(survmtest[,c(2,3)],"BESS:EBIC: MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.832 0.798 0.867 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 150 106 256
Test - 41 267 308
Total 191 373 564 

par(op)
ho <- mean(survmtest$Outcome)
timeInterval <- mean(survmtest[survmtest$Outcome==0,"Times"])
pgzero <- ppoisGzero(survmtest$LinearPredictorsMedian,ho)
rsdata <- cbind(survmtest$Outcome,pgzero,survmtest$Times)
riskAnalysis <- RRPlot(rsdata,riskTimeInterval=timeInterval,title="BESS:EBIC")

[1] 0.3386525 [1] 0.3386525 1.0000000 [1] 0.7771272 0.8672770 0.3942505 3.4912148 49.5056709 101.3358977 [7] 0.0000000 1.0000000 


pander::pander(riskAnalysis$c.index)

  • C Index: 0.818

  • Dxy: 0.636

  • S.D.: 0.0278

  • n: 564

  • missing: 0

  • uncensored: 191

  • Relevant Pairs: 142910

  • Concordant: 116890

  • Uncertain: 174472

  • cstatCI:

    mean.C Index median lower upper
    0.818 0.817 0.79 0.842 
pander::pander(riskAnalysis$ROCAnalysis$aucs)
est lower upper
0.831 0.796 0.866 
pander::pander(riskAnalysis$cenAUC)

0.853

pander::pander(riskAnalysis$ROCAnalysis$ClassMetrics)

  • accci:

    50% 2.5% 97.5%
    0.785 0.75 0.817

  • senci:

    50% 2.5% 97.5%
    0.633 0.564 0.7

  • speci:

    50% 2.5% 97.5%
    0.864 0.829 0.899

  • aucci:

    50% 2.5% 97.5%
    0.748 0.709 0.785

  • berci:

    50% 2.5% 97.5%
    0.252 0.215 0.291

  • preci:

    50% 2.5% 97.5%
    0.706 0.635 0.77

  • F1ci:

    50% 2.5% 97.5%
    0.668 0.608 0.721 
pander::pander(riskAnalysis$surdif)
Call: survival::Surv(eTime, eStatus) ~ class Chisq = 252.572826 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 334 49 133.7 53.6913 183.7155
class=1 58 21 20.3 0.0271 0.0306
class=2 172 121 37.0 190.6823 244.3976

1.1.3 Cross Validation GS


cvBESSGSERaw <- randomCV(TADPOLECrossMRI,
                 Surv(TimeToEvent,status)~.,
                 fittingFunction= BESS_GSECTION,
                 trainSampleSets= cvBSWiMSRaw$trainSamplesSets,
 )

.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51 Tested: 552 Avg. Selected: 50 Min Tests: 1 Max Tests: 10 Mean Tests: 5.036232 . MAD: 1.152137 .1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:32 s.split:39 s.right:44-th iteration s.left:39 s.split:42 s.right:44-th iteration s.left:42 s.split:43 s.right:44.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:32 s.split:39 s.right:44-th iteration s.left:39 s.split:42 s.right:44-th iteration s.left:42 s.split:43 s.right:44 Tested: 562 Avg. Selected: 49.3 Min Tests: 1 Max Tests: 17 Mean Tests: 9.893238 . MAD: 1.108346 .1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51 Tested: 564 Avg. Selected: 49.53333 Min Tests: 3 Max Tests: 25 Mean Tests: 14.78723 . MAD: 1.12239 .1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51 Tested: 564 Avg. Selected: 49.65 Min Tests: 4 Max Tests: 32 Mean Tests: 19.71631 . MAD: 1.119568 .1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51 Tested: 564 Avg. Selected: 49.72 Min Tests: 6 Max Tests: 39 Mean Tests: 24.64539 . MAD: 1.113176

pander::pander(cbind(cvBESSGSERaw$featureFrequency[cvBESSGSERaw$featureFrequency>20]))
FAQ 50
RAVLT_immediate 50
ADAS13 39
M_ST13TA 38
M_ST24CV 37
M_ST29SV 37
RD_ST34TA 37
RD_ST47TS 37
ABETA 34
RD_ST50TS 32
RD_ST12SV 31
M_ST66SV 30
RAVLT_learning 30
M_ST62SA 29
RD_ST129TS 29
WholeBrain 28
RD_ST44CV 27
RD_ST45TA 26
M_ST129CV 25
APOE4 24
PTAU 24
RD_ST31TA 24
RD_ST32TS 24
RD_ST51SA 24
RD_ST35TS 23
TAU 23
M_ST26TA 22
M_ST56CV 22
RD_ST24SA 22
RD_ST46TA 22
RD_ST58CV 22
M_ST31TA 21 
prBin <- predictionStats_binary(cvBESSGSERaw$survMedianTrain[,c(2,3)],"TRAIN: MCI to  AD Conversion")


survmtest <- cvBESSGSERaw$survMedianTest
survmtest <- survmtest[complete.cases(survmtest),]

prBin <- predictionStats_binary(survmtest[,c(2,3)],"BESS:GS: MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.838 0.804 0.872 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 156 104 260
Test - 35 269 304
Total 191 373 564 

par(op)
ho <- mean(survmtest$Outcome)
timeInterval <- mean(survmtest[survmtest$Outcome==0,"Times"])
pgzero <- ppoisGzero(survmtest$LinearPredictorsMedian,ho)
rsdata <- cbind(survmtest$Outcome,pgzero,survmtest$Times)
riskAnalysis <- RRPlot(rsdata,riskTimeInterval=timeInterval,title="BESS:GS")

[1] 0.3386525 [1] 0.3386525 1.0000000 [1] 0.7753446 0.8666518 0.3942505 3.4912148 49.7388732 96.6557288 0.0000000 [8] 1.0000000 


pander::pander(riskAnalysis$c.index)

  • C Index: 0.819

  • Dxy: 0.639

  • S.D.: 0.0264

  • n: 564

  • missing: 0

  • uncensored: 191

  • Relevant Pairs: 142910

  • Concordant: 117108

  • Uncertain: 174472

  • cstatCI:

    mean.C Index median lower upper
    0.819 0.82 0.794 0.847 
pander::pander(riskAnalysis$ROCAnalysis$aucs)
est lower upper
0.837 0.803 0.872 
pander::pander(riskAnalysis$cenAUC)

0.858

pander::pander(riskAnalysis$ROCAnalysis$ClassMetrics)

  • accci:

    50% 2.5% 97.5%
    0.787 0.755 0.819

  • senci:

    50% 2.5% 97.5%
    0.628 0.562 0.698

  • speci:

    50% 2.5% 97.5%
    0.869 0.834 0.902

  • aucci:

    50% 2.5% 97.5%
    0.749 0.712 0.786

  • berci:

    50% 2.5% 97.5%
    0.251 0.214 0.288

  • preci:

    50% 2.5% 97.5%
    0.711 0.637 0.779

  • F1ci:

    50% 2.5% 97.5%
    0.667 0.608 0.72 
pander::pander(riskAnalysis$surdif)
Call: survival::Surv(eTime, eStatus) ~ class Chisq = 267.336260 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 327 41 131.3 62.12 203.95
class=1 68 30 23.6 1.71 1.98
class=2 169 120 36.0 195.52 248.28

1.1.4 Learning BIC

bConvmBess <- BESS(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)

pander::pander(bConvmBess$selectedfeatures)

ADAS13, MMSE, RAVLT_immediate, RAVLT_learning, FAQ, APOE4, ABETA, PTAU, ST2SV, M_ST25TA, M_ST32TA, M_ST54TA, M_ST34TS, M_ST47TS, M_ST49TS, M_ST55TS, M_ST14SA, M_ST25SA, M_ST38SA, M_ST44SA, M_ST45SA, M_ST56SA, M_ST23CV, M_ST24CV, M_ST36CV, M_ST50CV, M_ST52CV, M_ST11SV, M_ST16SV, M_ST29SV, M_ST65SV, M_ST66SV, RD_ST13TA, RD_ST34TA, RD_ST36TA, RD_ST45TA, RD_ST47TA, RD_ST48TA, RD_ST51TA, RD_ST58TA, RD_ST60TA, RD_ST15TS, RD_ST23TS, RD_ST34TS, RD_ST36TS, RD_ST47TS, RD_ST50TS, RD_ST60TS, RD_ST23SA, RD_ST24SA, RD_ST25SA, RD_ST31SA, RD_ST34SA, RD_ST60SA, RD_ST129CV, RD_ST40CV, RD_ST43CV, RD_ST44CV, RD_ST47CV, RD_ST48CV, RD_ST49CV, RD_ST52CV, RD_ST56CV, RD_ST12SV, RD_ST29SV and RD_ST61SV


ptestl <- predict(bConvmBess,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBess,TADPOLE_Conv_TEST) - cval

boxplot(ptestl~TADPOLE_Conv_TEST$status)

ptestr <- exp(ptestl)

predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
                  TADPOLE_Conv_TEST$status,
                  ptestl,
                  ptestr)

prSurv <- predictionStats_survival(predsurv,"MCI to  AD Conversion")

pander::pander(prSurv$CIRisk)
median lower upper
0.847 0.799 0.887 
pander::pander(prSurv$CILp)
median lower upper
0.854 0.797 0.907 
pander::pander(prSurv$spearmanCI)
50% 2.5% 97.5%
0.449 0.218 0.628 

prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.854 0.797 0.912 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 48 35 83
Test - 10 77 87
Total 58 112 170 

par(op)

1.1.5 Learning EBIC

bConvmBessE <- BESS_EBIC(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)

pander::pander(bConvmBessE$selectedfeatures)

ADAS13, MMSE, RAVLT_immediate, RAVLT_learning, FAQ, APOE4, ABETA, PTAU, ST2SV, M_ST25TA, M_ST32TA, M_ST54TA, M_ST34TS, M_ST47TS, M_ST49TS, M_ST55TS, M_ST14SA, M_ST25SA, M_ST38SA, M_ST44SA, M_ST45SA, M_ST56SA, M_ST23CV, M_ST24CV, M_ST36CV, M_ST50CV, M_ST52CV, M_ST11SV, M_ST16SV, M_ST29SV, M_ST65SV, M_ST66SV, RD_ST13TA, RD_ST34TA, RD_ST36TA, RD_ST45TA, RD_ST47TA, RD_ST48TA, RD_ST51TA, RD_ST58TA, RD_ST60TA, RD_ST15TS, RD_ST23TS, RD_ST34TS, RD_ST36TS, RD_ST47TS, RD_ST50TS, RD_ST60TS, RD_ST23SA, RD_ST24SA, RD_ST25SA, RD_ST31SA, RD_ST34SA, RD_ST60SA, RD_ST129CV, RD_ST40CV, RD_ST43CV, RD_ST44CV, RD_ST47CV, RD_ST48CV, RD_ST49CV, RD_ST52CV, RD_ST56CV, RD_ST12SV, RD_ST29SV and RD_ST61SV


ptestl <- predict(bConvmBessE,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBessE,TADPOLE_Conv_TEST) - cval

boxplot(ptestl~TADPOLE_Conv_TEST$status)

ptestr <- exp(ptestl)

predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
                  TADPOLE_Conv_TEST$status,
                  ptestl,
                  ptestr)

prSurv <- predictionStats_survival(predsurv,"MCI to  AD Conversion")

pander::pander(prSurv$CIRisk)
median lower upper
0.863 0.816 0.903 
pander::pander(prSurv$CILp)
median lower upper
0.882 0.829 0.931 
pander::pander(prSurv$spearmanCI)
50% 2.5% 97.5%
0.48 0.265 0.659 

prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.882 0.832 0.931 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 50 26 76
Test - 8 86 94
Total 58 112 170 

par(op)

1.1.6 Learning GS

bConvmBessGS <- BESS_GSECTION(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)

1-th iteration s.left:1 s.split:41 s.right:66-th iteration s.left:41 s.split:56 s.right:66-th iteration s.left:56 s.split:62 s.right:66-th iteration s.left:62 s.split:64 s.right:66-th iteration s.left:64 s.split:65 s.right:66


pander::pander(bConvmBessGS$selectedfeatures)

ADAS11, ADAS13, RAVLT_immediate, RAVLT_learning, FAQ, APOE4, PTAU, ST68SV, M_ST15TA, M_ST26TA, M_ST32TA, M_ST38TA, M_ST44TA, M_ST45TA, M_ST50TA, M_ST52TA, M_ST57TA, M_ST59TA, M_ST23TS, M_ST60TS, M_ST35SA, M_ST48SA, M_ST24CV, M_ST129CV, M_ST36CV, M_ST38CV, M_ST44CV, M_ST52CV, M_ST56CV, M_ST58CV, M_ST11SV, M_ST16SV, RD_ST13TA, RD_ST34TA, RD_ST38TA, RD_ST45TA, RD_ST46TA, RD_ST47TA, RD_ST49TA, RD_ST50TA, RD_ST58TA, RD_ST60TA, RD_ST15TS, RD_ST26TS, RD_ST35TS, RD_ST46TS, RD_ST47TS, RD_ST59TS, RD_ST60TS, RD_ST23SA, RD_ST24SA, RD_ST25SA, RD_ST34SA, RD_ST13CV, RD_ST32CV, RD_ST44CV, RD_ST49CV, RD_ST62CV, RD_ST11SV, RD_ST12SV, RD_ST16SV, RD_ST30SV, RD_ST42SV, RD_ST61SV and RD_ST65SV


ptestl <- predict(bConvmBessGS,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBessGS,TADPOLE_Conv_TEST) - cval

boxplot(ptestl~TADPOLE_Conv_TEST$status)

ptestr <- exp(ptestl)

predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
                  TADPOLE_Conv_TEST$status,
                  ptestl,
                  ptestr)

prSurv <- predictionStats_survival(predsurv,"MCI to  AD Conversion")

pander::pander(prSurv$CIRisk)
median lower upper
0.788 0.731 0.84 
pander::pander(prSurv$CILp)
median lower upper
0.779 0.703 0.851 
pander::pander(prSurv$spearmanCI)
50% 2.5% 97.5%
0.409 0.149 0.612 

prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to  AD Conversion")

pander::pander(prBin$aucs)
est lower upper
0.777 0.706 0.848 
pander::pander(prBin$CM.analysis$tab)
  Outcome + Outcome - Total
Test + 44 40 84
Test - 14 72 86
Total 58 112 170 

par(op)

1.1.7 Saving the enviroment

save.image("./TADPOLE_BESS_Results.RData")